Previous work on e2e multilingual speech recognition systems has shown that a multilingual setup does not guarantee the reduction in Word Error Rate (WER) for target languages [1]}, [2]}, [3]}. Recent efforts to interpret the learning of multilingual speech recognition systems [4]}, [5]} observe that Phoneme Error Rate (PER) of an overlapped phoneme is not reduced with the growing number of sharing languages. The number of shared phonemes is not a reliable metric to measure language similarities and each participating language in a multilingual system has a different similarity with the target language. Even the balanced language data sampling can cause degradation or improvement due to internal acoustic-phonetic unbalancing [6]}. It demands very controlled language mixing for a target language ASR.
